One of the promising applications of automated script reading technology is the machine reading of script-written names and addresses in postal systems. To separate handwritten text such as street address and zip-code information into discrete digits and letters, the handwritten strokes or script must be separated. In the art of machine reading of script, this step is called "segmentation".
Typically, individual handwriting exhibits a characteristic global slant. By eliminating slant in a specimen of script, it becomes easier to machine-slice the script into its letter or number components. One approach to eliminating the slant factor, is to manipulate the individual handwriting strokes to maximize the verticality of the strokes. The characteristic slant angle thus is essentially removed; and the algorithms for operating on the still-connected script to separate it into discrete letters and numbers can then be deployed more successfully.
Obtaining a relatively reliable estimate of the global characteristic slant angle in a given specimen of script has proven difficult in practice. One reason is the wide individual variability of slant and style in handwriting. Slant angles encountered in script span a range of many degrees. Further, there is an uncertainty of knowing whether the slant component in a given element of script is due to the inherent shape of the letter/number, or to the writer's penmanship. Another reason is that some letters and numbers, even if consciously written without slant, have shapes that inherently contain slanted segments. Further, there is the fundamental question of how to define "characteristic slant angle" of script in terms that can be simply translated to manipulatable data elements.